Accurately modeling gross primary productivity (GPP) is of great importance for diagnosing
terrestrial carbon–climate feedbacks. Process-based terrestrial ecosystem models are often …

Partitioning evapotranspiration (ET) into vegetation transpiration (T) and soil evaporation (E)
is challenging, but it is key to improving the understanding of plant water use and changes in …

Gross primary production (GPP) by boreal forests is highly sensitive to environmental
changes. However, GPP simulated by land surface models (LSMs) remains highly uncertain …

Long‐term eddy covariance (EC) data are crucial for understanding the impact of global
change on ecosystem functions. However, EC data often contain long gaps, particularly in …

Although the maximum carboxylation rate (Vcmax) is an important parameter to calculate the
photosynthesis rate for the terrestrial biosphere models (TBMs), current models could not …

Accurate and efficient identification of pollution sources is a key process that assists in the
treatment of water pollution incidents. The ensemble Kalman filter (EnKF) has been proven …

Within the process of development of sustainable energy solutions, the Ensemble Kalman
Filter (EnKF) holds an allimportant key by assisting in forecasting and optimization of …

This paper presents a comprehensive study on the evolution, applications, and impact of the
Polarimetric Kalman Filter (PKF) in the fields of signal processing and remote sensing. By …

Abstract Ensemble Kalman filters (EnKFs) is a statistics assimilation technique extensively
used for the most influential kingdom estimation and forecasting. This chapter investigates …

陆地生态系统模型是研究全球碳循环与气候变化之间复杂反馈机制的重要工具. 然而,
陆地生态系统模型模拟具有很大的不确定性. 基于观测数据约束模型参数是实现模型的精确模拟 …